Toward high-resolution prediction and design of transmembrane helical protein structures

By Patrick Barth, J. Schonbrun, D. Baker. 2007

234 citations recorded by the source corpus.

The prediction and design at the atomic level of membrane protein structures and interactions is a critical but unsolved challenge. To address this problem, we have developed an all-atom physical model that describes intraprotein and protein–solvent interactions in the membrane environment. We evaluated the ability of the model to recapitulate the energetics and structural specificities of polytopic membrane proteins by using a battery of in silico prediction and design tests. First, in side-chain packing and design tests, the model successfully predicts the side-chain conformations at 73% of nonexposed positions and the native amino acid identities at 34% of positions in naturally occurring membrane proteins. Second, the model predicts significant energy gaps between native and nonnative structures of transmembrane helical interfaces and polytopic membrane proteins. Third, distortions in transmembrane helices are successfully recapitulated in docking experiments by using fragments of ideal helices judiciously defined around helical kinks. Finally, de novo structure prediction reaches near-atomic accuracy (<2.5 Å) for several small membrane protein domains (<150 residues). The success of the model highlights the critical role of van der Waals and hydrogen-bonding interactions in the stability and structural specificity of membrane protein structures and sets the stage for the high-resolution prediction and design of complex membrane protein architectures.

Subfields: Molecular Biology

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Citations in the corpus

The Rosetta all-atom energy function for macromolecular modeling and design (2017, methodology): “36,60,115,116 While these improve structure prediction accuracy, both models require more computation time.”
Robust and accurate prediction of residue–residue interactions across protein interfaces using evolutionary information (2014, methodology): “YIAM, a membrane protein, was modeled with the Rosetta membrane energy function ( Yarov-Yarovoy et al., 2012 , Barth et al., 2007 ).”
One contact for every twelve residues allows robust and accurate topology-level protein structure modeling (2013, positive): “Since the native structure is a dimer, As a last step, we refined our models using the Rosetta membrane energy function 42 with a high constraint weight but minimal structural changes to the models were made.”
An Integrated Framework Advancing Membrane Protein Modeling and Design (2015, methodology): “The RosettaMembrane ab initio protocol [23,26] was one of the first methods for ab initio structure prediction of membrane proteins.”
Accurate de novo structure prediction of large transmembrane protein domains using fragment-assembly and correlated mutation analysis (2012, background): “An adaptation of Rosetta, RosettaMembrane (14), added an energy function that described membrane intraprotein interactions at atomic level and membrane protein/lipid interactions implicitly while treating hydrogen bonds explicitly.”
Systematic evaluation of CS‐Rosetta for membrane protein structure prediction with sparse NOE restraints (2017, methodology): “The rationale behind this second approach is that the Rosetta all-atom energy function for membrane proteins might still leave room for improvement,17,34 so that model selection could benefit from not being limited to energy alone.”
Protein Structure Prediction and Design in a Biologically Realistic Implicit Membrane. (2020, methodology): “IMM1 has been applied to various biomolecular modeling problems, including studies of antimicrobial peptides (29), de novo folding (30), and de novo design of transmembrane helical bundles (10).”
Computational modeling of membrane proteins (2014, unknown): “Several tools are available, RosettaMembrane96,97 being one of the earliest and most reliable (Fig.”
Practically Useful: What the Rosetta Protein Modeling Suite Can Do for You (2010, background): “In 2007, Barth et al. used this potential to predict the structure of small trans-membrane helices at up to 2.5 A˚ rmsd ( 87 ).”
Enhancing structure prediction and design of soluble and membrane proteins with explicit solvent-protein interactions (2017, background): “As described in Barth et al., 2007, an interpolation function models the transition between the high dielectric lipid headgroup and low dielectric hydrophobic core regions of the membrane for estimating the desolvation costs at different depths in the membrane.”